import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns
import plotly.express as px #for visualization
import matplotlib.pyplot as plt #for visualization 

df100=pd.read_csv("C:\\Users\\2001n\\OneDrive\\Documents\\CHURN_DATASET.csv")
df100

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	...	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	2234-XADUH	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	...	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	4801-JZAZL	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	8361-LTMKD	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	3186-AJIEK	Male	0	No	No	66	Yes	No	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

7043 rows × 21 columns

df=df100.drop(["customerID"],axis = 1)
df

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	Male	0	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

7043 rows × 20 columns

df.shape

(7043, 20)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   gender            7043 non-null   object 
 1   SeniorCitizen     7043 non-null   int64  
 2   Partner           7043 non-null   object 
 3   Dependents        7043 non-null   object 
 4   tenure            7043 non-null   int64  
 5   PhoneService      7043 non-null   object 
 6   MultipleLines     7043 non-null   object 
 7   InternetService   7043 non-null   object 
 8   OnlineSecurity    7043 non-null   object 
 9   OnlineBackup      7043 non-null   object 
 10  DeviceProtection  7043 non-null   object 
 11  TechSupport       7043 non-null   object 
 12  StreamingTV       7043 non-null   object 
 13  StreamingMovies   7043 non-null   object 
 14  Contract          7043 non-null   object 
 15  PaperlessBilling  7043 non-null   object 
 16  PaymentMethod     7043 non-null   object 
 17  MonthlyCharges    7043 non-null   float64
 18  TotalCharges      7043 non-null   object 
 19  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(17)
memory usage: 1.1+ MB

def dataoveriew(df, message):
    
    print(f'{message}:n')  # it contains expressions in the braces
   
    print('Number of rows: '   , df.shape[0])
    print("Number of features:", df.shape[1])
    print(df.columns.tolist())
    print("Missing values:", df.isnull().sum().values.sum())
    print("Unique values:")
    print(df.nunique())

dataoveriew(df, 'Overview of the dataset')

Overview of the dataset:n
Number of rows:  7043
Number of features: 20
['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']
Missing values: 0
Unique values:
gender                 2
SeniorCitizen          2
Partner                2
Dependents             2
tenure                73
PhoneService           2
MultipleLines          3
InternetService        3
OnlineSecurity         3
OnlineBackup           3
DeviceProtection       3
TechSupport            3
StreamingTV            3
StreamingMovies        3
Contract               3
PaperlessBilling       2
PaymentMethod          4
MonthlyCharges      1585
TotalCharges        6531
Churn                  2
dtype: int64

missing = pd.DataFrame((df.isnull().sum())*100/df.shape[0]).reset_index()

plt.figure(figsize=(16,5))

sns.pointplot(x ='index',y = 0,data=missing)

plt.xticks(rotation =90,fontsize =7)

plt.title("Percentage of Missing values")
plt.ylabel("PERCENTAGE")

plt.show()

df.TotalCharges = pd.to_numeric(df.TotalCharges, errors='coerce')
df.isnull().sum()

gender               0
SeniorCitizen        0
Partner              0
Dependents           0
tenure               0
PhoneService         0
MultipleLines        0
InternetService      0
OnlineSecurity       0
OnlineBackup         0
DeviceProtection     0
TechSupport          0
StreamingTV          0
StreamingMovies      0
Contract             0
PaperlessBilling     0
PaymentMethod        0
MonthlyCharges       0
TotalCharges        11
Churn                0
dtype: int64

df.loc[df['TotalCharges'].isnull() == True]

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
488	Female	0	Yes	Yes	0	No	No phone service	DSL	Yes	No	Yes	Yes	Yes	No	Two year	Yes	Bank transfer (automatic)	52.55	NaN	No
753	Male	0	No	Yes	0	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	20.25	NaN	No
936	Female	0	Yes	Yes	0	Yes	No	DSL	Yes	Yes	Yes	No	Yes	Yes	Two year	No	Mailed check	80.85	NaN	No
1082	Male	0	Yes	Yes	0	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	25.75	NaN	No
1340	Female	0	Yes	Yes	0	No	No phone service	DSL	Yes	Yes	Yes	Yes	Yes	No	Two year	No	Credit card (automatic)	56.05	NaN	No
3331	Male	0	Yes	Yes	0	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	19.85	NaN	No
3826	Male	0	Yes	Yes	0	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	25.35	NaN	No
4380	Female	0	Yes	Yes	0	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	20.00	NaN	No
5218	Male	0	Yes	Yes	0	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	Yes	Mailed check	19.70	NaN	No
6670	Female	0	Yes	Yes	0	Yes	Yes	DSL	No	Yes	Yes	Yes	Yes	No	Two year	No	Mailed check	73.35	NaN	No
6754	Male	0	No	Yes	0	Yes	Yes	DSL	Yes	Yes	No	Yes	No	No	Two year	Yes	Bank transfer (automatic)	61.90	NaN	No

df.dropna(how = 'any', inplace = True)

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 7032 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   gender            7032 non-null   object 
 1   SeniorCitizen     7032 non-null   int64  
 2   Partner           7032 non-null   object 
 3   Dependents        7032 non-null   object 
 4   tenure            7032 non-null   int64  
 5   PhoneService      7032 non-null   object 
 6   MultipleLines     7032 non-null   object 
 7   InternetService   7032 non-null   object 
 8   OnlineSecurity    7032 non-null   object 
 9   OnlineBackup      7032 non-null   object 
 10  DeviceProtection  7032 non-null   object 
 11  TechSupport       7032 non-null   object 
 12  StreamingTV       7032 non-null   object 
 13  StreamingMovies   7032 non-null   object 
 14  Contract          7032 non-null   object 
 15  PaperlessBilling  7032 non-null   object 
 16  PaymentMethod     7032 non-null   object 
 17  MonthlyCharges    7032 non-null   float64
 18  TotalCharges      7032 non-null   float64
 19  Churn             7032 non-null   object 
dtypes: float64(2), int64(2), object(16)
memory usage: 1.1+ MB

df.shape

(7032, 20)

df1 = df["Churn"].value_counts().reset_index()  # Reset index to create columns, Defult index
df1.columns = ['Category', 'count']  # Rename columns explicitly

df1

	Category	count
0	No	5163
1	Yes	1869

df1 = df["Churn"].value_counts().reset_index()  # Reset index to create columns, Defult index
df1.columns = ['Category', 'count']  # Rename columns explicitly

fig = px.pie(df1, values='count', names='Category',color_discrete_sequence=["green", "red"],
            title='Distribution of Churn')
fig.show()

df2 = df.copy()

labels = ["{0} - {1}".format(i, i + 11) for i in range(1, 72, 12)]

df2['tenure_group'] = pd.cut(df.tenure, range(1, 80, 12), right=False, labels=labels)

df2.tenure_group.value_counts()

tenure_group
1 - 12     2175
61 - 72    1407
13 - 24    1024
25 - 36     832
49 - 60     832
37 - 48     762
Name: count, dtype: int64

df2

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	tenure_group
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	...	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No	1 - 12
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	...	No	No	No	One year	No	Mailed check	56.95	1889.50	No	25 - 36
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	...	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes	1 - 12
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	...	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No	37 - 48
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	...	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes	1 - 12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	...	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.50	No	13 - 24
7039	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	...	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.90	No	61 - 72
7040	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	...	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No	1 - 12
7041	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	...	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.60	Yes	1 - 12
7042	Male	0	No	No	66	Yes	No	Fiber optic	Yes	No	...	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.50	No	61 - 72

7032 rows × 21 columns

df2.shape

(7032, 21)

import warnings
warnings.filterwarnings('ignore')

df2.drop(columns= ['tenure'], axis=1, inplace=True)
df2

	gender	SeniorCitizen	Partner	Dependents	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	tenure_group
0	Female	0	Yes	No	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No	1 - 12
1	Male	0	No	No	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No	25 - 36
2	Male	0	No	No	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes	1 - 12
3	Male	0	No	No	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No	37 - 48
4	Female	0	No	No	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes	1 - 12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	Male	0	Yes	Yes	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.50	No	13 - 24
7039	Female	0	Yes	Yes	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.90	No	61 - 72
7040	Female	0	Yes	Yes	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No	1 - 12
7041	Male	1	Yes	No	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.60	Yes	1 - 12
7042	Male	0	No	No	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.50	No	61 - 72

7032 rows × 20 columns

for i, predictor in enumerate(df2.drop(columns=['Churn', 'TotalCharges', 'MonthlyCharges'])):
    plt.figure(i)
    plt.xticks(rotation = 90)
    sns.countplot(data=df2, x=predictor, hue='Churn')

def hist(feature):
   
    df2 = df.groupby([feature, 'Churn']).size().reset_index()
    df2 = df2.rename(columns={0: 'Count'})  # Corrected line
    
    fig = px.histogram(df2, x=feature, y='Count', color='Churn', marginal='box'
                       , title=('Churn rate frequency to distribution')
                       , color_discrete_sequence=["green", "red"])
    fig.show()

hist('tenure')
hist('MonthlyCharges')
hist('TotalCharges')

bin_df = pd.DataFrame()

#Update the binning dataframe

bin_df['tenure_bins'] =  pd.qcut(df['tenure'], q=3, labels= ['low', 'medium', 'high'])

bin_df['MonthlyCharges_bins'] =  pd.qcut(df['MonthlyCharges'], q=3, labels= ['low', 'medium', 'high'])

bin_df['TotalCharges_bins'] =  pd.qcut(df['TotalCharges'], q=3, labels= ['low', 'medium', 'high'])

bin_df['Churn'] = df['Churn']

def plot_bar(feature, data):
    fig = px.bar(data.groupby([feature, 'Churn']).size().reset_index(), 
                 x=feature, y=0, color='Churn', 
                 title=f'Churn rate frequency to {feature} bins', 
                 barmode='group',
                 color_discrete_sequence=["green", "red"])
    fig.show()

plot_bar('tenure_bins', bin_df)
plot_bar('MonthlyCharges_bins', bin_df)
plot_bar('TotalCharges_bins', bin_df)

DATA PREPROCESSING

from ydata_profiling import ProfileReport
profile = ProfileReport(df, title="Profiling Report")

profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

from sklearn import preprocessing

df3 = pd.get_dummies(df2, columns=['gender','Partner','Dependents','tenure_group','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod','Churn'])

df3

	SeniorCitizen	MonthlyCharges	TotalCharges	gender_Female	gender_Male	Partner_No	Partner_Yes	Dependents_No	Dependents_Yes	tenure_group_1 - 12	...	Contract_One year	Contract_Two year	PaperlessBilling_No	PaperlessBilling_Yes	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_No	Churn_Yes
0	0	29.85	29.85	True	False	False	True	True	False	True	...	False	False	False	True	False	False	True	False	True	False
1	0	56.95	1889.50	False	True	True	False	True	False	False	...	True	False	True	False	False	False	False	True	True	False
2	0	53.85	108.15	False	True	True	False	True	False	True	...	False	False	False	True	False	False	False	True	False	True
3	0	42.30	1840.75	False	True	True	False	True	False	False	...	True	False	True	False	True	False	False	False	True	False
4	0	70.70	151.65	True	False	True	False	True	False	True	...	False	False	False	True	False	False	True	False	False	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0	84.80	1990.50	False	True	False	True	False	True	False	...	True	False	False	True	False	False	False	True	True	False
7039	0	103.20	7362.90	True	False	False	True	False	True	False	...	True	False	False	True	False	True	False	False	True	False
7040	0	29.60	346.45	True	False	False	True	False	True	True	...	False	False	False	True	False	False	True	False	True	False
7041	1	74.40	306.60	False	True	False	True	True	False	True	...	False	False	False	True	False	False	False	True	False	True
7042	0	105.65	6844.50	False	True	True	False	True	False	False	...	False	True	False	True	True	False	False	False	True	False

7032 rows × 52 columns

df4 = df3.replace({True :1,False: 0})

df4

	SeniorCitizen	MonthlyCharges	TotalCharges	gender_Female	gender_Male	Partner_No	Partner_Yes	Dependents_No	Dependents_Yes	tenure_group_1 - 12	...	Contract_One year	Contract_Two year	PaperlessBilling_No	PaperlessBilling_Yes	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_No	Churn_Yes
0	0	29.85	29.85	1	0	0	1	1	0	1	...	0	0	0	1	0	0	1	0	1	0
1	0	56.95	1889.50	0	1	1	0	1	0	0	...	1	0	1	0	0	0	0	1	1	0
2	0	53.85	108.15	0	1	1	0	1	0	1	...	0	0	0	1	0	0	0	1	0	1
3	0	42.30	1840.75	0	1	1	0	1	0	0	...	1	0	1	0	1	0	0	0	1	0
4	0	70.70	151.65	1	0	1	0	1	0	1	...	0	0	0	1	0	0	1	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0	84.80	1990.50	0	1	0	1	0	1	0	...	1	0	0	1	0	0	0	1	1	0
7039	0	103.20	7362.90	1	0	0	1	0	1	0	...	1	0	0	1	0	1	0	0	1	0
7040	0	29.60	346.45	1	0	0	1	0	1	1	...	0	0	0	1	0	0	1	0	1	0
7041	1	74.40	306.60	0	1	0	1	1	0	1	...	0	0	0	1	0	0	0	1	0	1
7042	0	105.65	6844.50	0	1	1	0	1	0	0	...	0	1	0	1	1	0	0	0	1	0

7032 rows × 52 columns

# Step 1: Exclude non-numeric columns
numeric_df = df4.select_dtypes(include=['number'])

# Step 2: Check for non-numeric columns with string values
non_numeric_cols = df4.columns.difference(numeric_df.columns)

# If there are non-numeric columns with string values, you can drop them or convert them to numeric if possible.
# For example, to drop them:
df4 = df4.drop(columns=non_numeric_cols)

# Now, you can calculate the correlation matrix
c_matrix = df4.corr()

c_matrix

	SeniorCitizen	MonthlyCharges	TotalCharges	gender_Female	gender_Male	Partner_No	Partner_Yes	Dependents_No	Dependents_Yes	tenure_group_1 - 12	...	Contract_One year	Contract_Two year	PaperlessBilling_No	PaperlessBilling_Yes	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_No	Churn_Yes
SeniorCitizen	1.000000	0.219874	0.102411	0.001819	-0.001819	-0.016957	0.016957	0.210550	-0.210550	-0.027713	...	-0.046491	-0.116205	-0.156258	0.156258	-0.016235	-0.024359	0.171322	-0.152987	-0.150541	0.150541
MonthlyCharges	0.219874	1.000000	0.651065	0.013779	-0.013779	-0.097825	0.097825	0.112343	-0.112343	-0.191881	...	0.004810	-0.073256	-0.351930	0.351930	0.042410	0.030055	0.271117	-0.376568	-0.192858	0.192858
TotalCharges	0.102411	0.651065	1.000000	-0.000048	0.000048	-0.319072	0.319072	-0.064653	0.064653	-0.592443	...	0.170569	0.358036	-0.157830	0.157830	0.186119	0.182663	-0.060436	-0.294708	0.199484	-0.199484
gender_Female	0.001819	0.013779	-0.000048	1.000000	-1.000000	-0.001379	0.001379	0.010349	-0.010349	0.001050	...	-0.007755	0.003603	-0.011902	0.011902	0.015973	-0.001632	-0.000844	-0.013199	-0.008545	0.008545
gender_Male	-0.001819	-0.013779	0.000048	-1.000000	1.000000	0.001379	-0.001379	-0.010349	0.010349	-0.001050	...	0.007755	-0.003603	0.011902	-0.011902	-0.015973	0.001632	0.000844	0.013199	0.008545	-0.008545
Partner_No	-0.016957	-0.097825	-0.319072	-0.001379	0.001379	1.000000	-1.000000	0.452269	-0.452269	0.305061	...	-0.083067	-0.247334	-0.013957	0.013957	-0.111406	-0.082327	0.083207	0.096948	-0.149982	0.149982
Partner_Yes	0.016957	0.097825	0.319072	0.001379	-0.001379	-1.000000	1.000000	-0.452269	0.452269	-0.305061	...	0.083067	0.247334	0.013957	-0.013957	0.111406	0.082327	-0.083207	-0.096948	0.149982	-0.149982
Dependents_No	0.210550	0.112343	-0.064653	0.010349	-0.010349	0.452269	-0.452269	1.000000	-1.000000	0.145379	...	-0.069222	-0.201699	-0.110131	0.110131	-0.052369	-0.061134	0.149274	-0.056448	-0.163128	0.163128
Dependents_Yes	-0.210550	-0.112343	0.064653	-0.010349	0.010349	-0.452269	0.452269	-1.000000	1.000000	-0.145379	...	0.069222	0.201699	0.110131	-0.110131	0.052369	0.061134	-0.149274	0.056448	0.163128	-0.163128
tenure_group_1 - 12	-0.027713	-0.191881	-0.592443	0.001050	-0.001050	0.305061	-0.305061	0.145379	-0.145379	1.000000	...	-0.251299	-0.333850	0.003860	-0.003860	-0.185855	-0.184165	0.160530	0.183222	-0.319628	0.319628
tenure_group_13 - 24	0.001860	-0.047220	-0.210745	0.000649	-0.000649	0.048481	-0.048481	0.001459	-0.001459	-0.276268	...	-0.017196	-0.146749	-0.003328	0.003328	-0.046329	-0.039647	0.030387	0.050371	-0.019929	0.019929
tenure_group_25 - 36	0.027317	0.009465	-0.047370	-0.006249	0.006249	-0.003131	0.003131	-0.009289	0.009289	-0.245138	...	0.082077	-0.106618	0.005504	-0.005504	-0.000472	0.008599	0.003897	-0.012360	0.040997	-0.040997
tenure_group_37 - 48	-0.000929	0.017614	0.083696	0.020658	-0.020658	-0.035092	0.035092	-0.023544	0.023544	-0.233286	...	0.122003	-0.004919	0.001538	-0.001538	0.029750	0.029093	-0.019634	-0.035775	0.059579	-0.059579
tenure_group_49 - 60	0.014186	0.070048	0.252905	0.004319	-0.004319	-0.105341	0.105341	-0.031419	0.031419	-0.245138	...	0.158917	0.080082	-0.010626	0.010626	0.060183	0.048167	-0.030584	-0.072170	0.100800	-0.100800
tenure_group_61 - 72	-0.002407	0.185440	0.639312	-0.016279	0.016279	-0.280353	0.280353	-0.118090	0.118090	-0.334681	...	0.016142	0.540336	0.001415	-0.001415	0.184249	0.179294	-0.175456	-0.160053	0.226078	-0.226078
PhoneService_No	-0.008392	-0.248033	-0.113008	-0.007515	0.007515	0.018397	-0.018397	-0.001078	0.001078	0.006950	...	0.003142	-0.004442	0.016696	-0.016696	-0.008271	0.006916	-0.002747	0.004463	0.011691	-0.011691
PhoneService_Yes	0.008392	0.248033	0.113008	0.007515	-0.007515	-0.018397	0.018397	0.001078	-0.001078	-0.006950	...	-0.003142	0.004442	-0.016696	0.016696	0.008271	-0.006916	0.002747	-0.004463	-0.011691	0.011691
MultipleLines_No	-0.136377	-0.338514	-0.396765	-0.004335	0.004335	0.130028	-0.130028	-0.023388	0.023388	0.256171	...	0.001694	-0.102756	0.151974	-0.151974	-0.069663	-0.063712	-0.080990	0.222395	0.032654	-0.032654
MultipleLines_No phone service	-0.008392	-0.248033	-0.113008	-0.007515	0.007515	0.018397	-0.018397	-0.001078	0.001078	0.006950	...	0.003142	-0.004442	0.016696	-0.016696	-0.008271	0.006916	-0.002747	0.004463	0.011691	-0.011691
MultipleLines_Yes	0.142996	0.490912	0.469042	0.008883	-0.008883	-0.142561	0.142561	0.024307	-0.024307	-0.263331	...	-0.003594	0.106618	-0.163746	0.163746	0.075429	0.060319	0.083583	-0.227672	-0.040033	0.040033
InternetService_DSL	-0.108276	-0.161368	-0.052190	-0.007584	0.007584	0.001043	-0.001043	-0.051593	0.051593	-0.001470	...	0.047300	0.030924	0.063390	-0.063390	0.024760	0.051222	-0.104293	0.042754	0.124141	-0.124141
InternetService_Fiber optic	0.254923	0.787195	0.360769	0.011189	-0.011189	-0.001235	0.001235	0.164101	-0.164101	-0.021441	...	-0.076809	-0.209965	-0.326470	0.326470	-0.022779	-0.050552	0.335763	-0.305984	-0.307463	0.307463
InternetService_No	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
OnlineSecurity_No	0.185145	0.360220	-0.064515	-0.010859	0.010859	0.129394	-0.129394	0.186979	-0.186979	0.196529	...	-0.122360	-0.352447	-0.267592	0.267592	-0.084436	-0.105963	0.335854	-0.190919	-0.342235	0.342235
OnlineSecurity_No internet service	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
OnlineSecurity_Yes	-0.038576	0.296447	0.412619	0.016328	-0.016328	-0.143346	0.143346	-0.080786	0.080786	-0.242409	...	0.100658	0.191698	0.004051	-0.004051	0.094366	0.115473	-0.112295	-0.079918	0.171270	-0.171270
OnlineBackup_No	0.087539	0.210126	-0.177633	-0.008605	0.008605	0.135626	-0.135626	0.137421	-0.137421	0.233228	...	-0.112133	-0.287128	-0.144218	0.144218	-0.082365	-0.088189	0.236414	-0.098438	-0.267595	0.267595
OnlineBackup_No internet service	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
OnlineBackup_Yes	0.066663	0.441529	0.510100	0.013093	-0.013093	-0.141849	0.141849	-0.023639	0.023639	-0.267366	...	0.084113	0.111391	-0.127056	0.127056	0.086942	0.090455	-0.000364	-0.174075	0.082307	-0.082307
DeviceProtection_No	0.094403	0.171057	-0.189485	0.003163	-0.003163	0.146702	-0.146702	0.128053	-0.128053	0.239267	...	-0.130038	-0.338520	-0.166253	0.166253	-0.078561	-0.108008	0.239173	-0.085850	-0.252056	0.252056
DeviceProtection_No internet service	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
DeviceProtection_Yes	0.059514	0.482607	0.522881	0.000807	-0.000807	-0.153556	0.153556	-0.013900	0.013900	-0.273920	...	0.102911	0.165248	-0.104079	0.104079	0.083047	0.111252	-0.003308	-0.187325	0.066193	-0.066193
TechSupport_No	0.205254	0.321267	-0.084270	-0.003815	0.003815	0.108875	-0.108875	0.171164	-0.171164	0.193915	...	-0.118709	-0.397788	-0.229875	0.229875	-0.090296	-0.107761	0.338529	-0.186388	-0.336877	0.336877
TechSupport_No internet service	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
TechSupport_Yes	-0.060577	0.338301	0.432868	0.008507	-0.008507	-0.120206	0.120206	-0.063053	0.063053	-0.238628	...	0.096258	0.240924	-0.037536	0.037536	0.100472	0.117024	-0.114807	-0.084631	0.164716	-0.164716
StreamingTV_No	0.048664	0.016015	-0.197144	-0.003088	0.003088	0.123394	-0.123394	0.099912	-0.099912	0.196100	...	-0.093495	-0.254456	-0.046715	0.046715	-0.044887	-0.041309	0.095426	-0.022650	-0.128435	0.128435
StreamingTV_No internet service	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
StreamingTV_Yes	0.105445	0.629668	0.515709	0.007124	-0.007124	-0.124483	0.124483	0.016499	-0.016499	-0.220761	...	0.061930	0.072124	-0.224241	0.224241	0.046121	0.040010	0.144747	-0.247712	-0.063254	0.063254
StreamingMovies_No	0.034196	0.017271	-0.202605	-0.006078	0.006078	0.117488	-0.117488	0.078245	-0.078245	0.197479	...	-0.096613	-0.258495	-0.058987	0.058987	-0.047677	-0.049817	0.102617	-0.019648	-0.130920	0.130920
StreamingMovies_No internet service	-0.182519	-0.763191	-0.374878	-0.004745	0.004745	0.000286	-0.000286	-0.138383	0.138383	0.027554	...	0.038061	0.217542	0.320592	-0.320592	-0.001094	0.001870	-0.284608	0.319694	0.227578	-0.227578
StreamingMovies_Yes	0.119842	0.627235	0.519867	0.010105	-0.010105	-0.118108	0.118108	0.038375	-0.038375	-0.221388	...	0.064780	0.075603	-0.211583	0.211583	0.048755	0.048398	0.137420	-0.250290	-0.060860	0.060860
Contract_Month-to-month	0.137752	0.058933	-0.446776	0.003251	-0.003251	0.280202	-0.280202	0.229715	-0.229715	0.492052	...	-0.570053	-0.621933	-0.168296	0.168296	-0.180159	-0.204960	0.330879	0.006209	-0.404565	0.404565
Contract_One year	-0.046491	0.004810	0.170569	-0.007755	0.007755	-0.083067	0.083067	-0.069222	0.069222	-0.251299	...	1.000000	-0.288843	0.052278	-0.052278	0.057629	0.067590	-0.109546	0.000197	0.178225	-0.178225
Contract_Two year	-0.116205	-0.073256	0.358036	0.003603	-0.003603	-0.247334	0.247334	-0.201699	0.201699	-0.333850	...	-0.288843	1.000000	0.146281	-0.146281	0.155004	0.174410	-0.281147	-0.007423	0.301552	-0.301552
PaperlessBilling_No	-0.156258	-0.351930	-0.157830	-0.011902	0.011902	-0.013957	0.013957	-0.110131	0.110131	0.003860	...	0.052278	0.146281	1.000000	-1.000000	0.017469	0.013726	-0.208427	0.203981	0.191454	-0.191454
PaperlessBilling_Yes	0.156258	0.351930	0.157830	0.011902	-0.011902	0.013957	-0.013957	0.110131	-0.110131	-0.003860	...	-0.052278	-0.146281	-1.000000	1.000000	-0.017469	-0.013726	0.208427	-0.203981	-0.191454	0.191454
PaymentMethod_Bank transfer (automatic)	-0.016235	0.042410	0.186119	0.015973	-0.015973	-0.111406	0.111406	-0.052369	0.052369	-0.185855	...	0.057629	0.155004	0.017469	-0.017469	1.000000	-0.278423	-0.377270	-0.288097	0.118136	-0.118136
PaymentMethod_Credit card (automatic)	-0.024359	0.030055	0.182663	-0.001632	0.001632	-0.082327	0.082327	-0.061134	0.061134	-0.184165	...	0.067590	0.174410	0.013726	-0.013726	-0.278423	1.000000	-0.373978	-0.285583	0.134687	-0.134687
PaymentMethod_Electronic check	0.171322	0.271117	-0.060436	-0.000844	0.000844	0.083207	-0.083207	0.149274	-0.149274	0.160530	...	-0.109546	-0.281147	-0.208427	0.208427	-0.377270	-0.373978	1.000000	-0.386971	-0.301455	0.301455
PaymentMethod_Mailed check	-0.152987	-0.376568	-0.294708	-0.013199	0.013199	0.096948	-0.096948	-0.056448	0.056448	0.183222	...	0.000197	-0.007423	0.203981	-0.203981	-0.288097	-0.285583	-0.386971	1.000000	0.090773	-0.090773
Churn_No	-0.150541	-0.192858	0.199484	-0.008545	0.008545	-0.149982	0.149982	-0.163128	0.163128	-0.319628	...	0.178225	0.301552	0.191454	-0.191454	0.118136	0.134687	-0.301455	0.090773	1.000000	-1.000000
Churn_Yes	0.150541	0.192858	-0.199484	0.008545	-0.008545	0.149982	-0.149982	0.163128	-0.163128	0.319628	...	-0.178225	-0.301552	-0.191454	0.191454	-0.118136	-0.134687	0.301455	-0.090773	-1.000000	1.000000

52 rows × 52 columns

plt.figure(figsize = (12,12))
sns.heatmap(data = c_matrix,  cmap = "Paired")

<Axes: >

SPLITTING THE DATASET

x = df4.drop(['Churn_Yes','Churn_No'],axis =1)
y = df4['Churn_Yes']

x

	SeniorCitizen	MonthlyCharges	TotalCharges	gender_Female	gender_Male	Partner_No	Partner_Yes	Dependents_No	Dependents_Yes	tenure_group_1 - 12	...	StreamingMovies_Yes	Contract_Month-to-month	Contract_One year	Contract_Two year	PaperlessBilling_No	PaperlessBilling_Yes	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
0	0	29.85	29.85	1	0	0	1	1	0	1	...	0	1	0	0	0	1	0	0	1	0
1	0	56.95	1889.50	0	1	1	0	1	0	0	...	0	0	1	0	1	0	0	0	0	1
2	0	53.85	108.15	0	1	1	0	1	0	1	...	0	1	0	0	0	1	0	0	0	1
3	0	42.30	1840.75	0	1	1	0	1	0	0	...	0	0	1	0	1	0	1	0	0	0
4	0	70.70	151.65	1	0	1	0	1	0	1	...	0	1	0	0	0	1	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0	84.80	1990.50	0	1	0	1	0	1	0	...	1	0	1	0	0	1	0	0	0	1
7039	0	103.20	7362.90	1	0	0	1	0	1	0	...	1	0	1	0	0	1	0	1	0	0
7040	0	29.60	346.45	1	0	0	1	0	1	1	...	0	1	0	0	0	1	0	0	1	0
7041	1	74.40	306.60	0	1	0	1	1	0	1	...	0	1	0	0	0	1	0	0	0	1
7042	0	105.65	6844.50	0	1	1	0	1	0	0	...	1	0	0	1	0	1	1	0	0	0

7032 rows × 50 columns

y

0       0
1       0
2       1
3       0
4       1
       ..
7038    0
7039    0
7040    0
7041    1
7042    0
Name: Churn_Yes, Length: 7032, dtype: int64

 from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 10)

len(x_train)

5625

len(x_test)

1407

from sklearn.metrics import mean_squared_error,confusion_matrix,classification_report,accuracy_score
from sklearn.metrics import roc_curve,roc_auc_score,accuracy_score,mean_absolute_error
from sklearn.metrics import r2_score,mean_squared_error

from sklearn.linear_model import LinearRegression
model = LinearRegression()

model.fit(x_train,y_train)

LinearRegression()

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model.score(x_test,y_test) # Accuracy

0.3084976908935517

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

model.predict(x_test)

array([-0.07128335,  0.02163858,  0.14581771, ..., -0.0334702 ,
        0.19590921,  0.33938377])

y_test

5401    0
1681    0
2076    0
3359    0
6629    0
       ..
1986    0
5608    0
6400    0
5560    0
3853    0
Name: Churn_Yes, Length: 1407, dtype: int64

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()

model.fit(x_train,y_train)

LogisticRegression()

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model.score(x_test,y_test)

0.8130774697938877

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[944, 102],
       [161, 200]], dtype=int64)

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

from sklearn import tree

model = tree.DecisionTreeClassifier()

model.fit(x_train,y_train)

DecisionTreeClassifier()

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model.score(x_test,y_test)

0.7320540156361052

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[825, 221],
       [156, 205]], dtype=int64)

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.84      0.79      0.81      1046
           1       0.48      0.57      0.52       361

    accuracy                           0.73      1407
   macro avg       0.66      0.68      0.67      1407
weighted avg       0.75      0.73      0.74      1407

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

from sklearn.svm import SVC
model = SVC()

model.fit(x_train,y_train)

SVC()

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model.score(x_test,y_test)

0.7434257285003554

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[1046,    0],
       [ 361,    0]], dtype=int64)

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.74      1.00      0.85      1046
           1       0.00      0.00      0.00       361

    accuracy                           0.74      1407
   macro avg       0.37      0.50      0.43      1407
weighted avg       0.55      0.74      0.63      1407

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

# multiple algo to predict outcome
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()

model.fit(x_train,y_train)

RandomForestClassifier()

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model.score(x_test,y_test)

0.7931769722814499

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[919, 127],
       [164, 197]], dtype=int64)

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.85      0.88      0.86      1046
           1       0.61      0.55      0.58       361

    accuracy                           0.79      1407
   macro avg       0.73      0.71      0.72      1407
weighted avg       0.79      0.79      0.79      1407

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

from sklearn.naive_bayes import GaussianNB
model = GaussianNB()

model.fit(x_train,y_train)

GaussianNB()

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model.score(x_test,y_test)

0.689410092395167

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[664, 382],
       [ 55, 306]], dtype=int64)

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.92      0.63      0.75      1046
           1       0.44      0.85      0.58       361

    accuracy                           0.69      1407
   macro avg       0.68      0.74      0.67      1407
weighted avg       0.80      0.69      0.71      1407

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB()

model.fit(x_train,y_train)

MultinomialNB()

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model.score(x_test,y_test)

0.658137882018479

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[637, 409],
       [ 72, 289]], dtype=int64)

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.90      0.61      0.73      1046
           1       0.41      0.80      0.55       361

    accuracy                           0.66      1407
   macro avg       0.66      0.70      0.64      1407
weighted avg       0.77      0.66      0.68      1407

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

scaler.fit(x_test,y_test)

StandardScaler()

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scaler.fit_transform(x)

array([[-0.44032709, -1.16169394, -0.99419409, ..., -0.5253508 ,
         1.40476387, -0.54360352],
       [-0.44032709, -0.26087792, -0.17373982, ..., -0.5253508 ,
        -0.71186341,  1.83957601],
       [-0.44032709, -0.36392329, -0.95964911, ..., -0.5253508 ,
        -0.71186341,  1.83957601],
       ...,
       [-0.44032709, -1.17000405, -0.85451414, ..., -0.5253508 ,
         1.40476387, -0.54360352],
       [ 2.27103902,  0.31916782, -0.87209546, ..., -0.5253508 ,
        -0.71186341,  1.83957601],
       [-0.44032709,  1.35793167,  2.01234407, ..., -0.5253508 ,
        -0.71186341, -0.54360352]])

from sklearn.preprocessing import MinMaxScaler
model = MinMaxScaler()

model.fit(x_train,y_train)

MinMaxScaler()

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model.fit_transform(x)

array([[0.        , 0.11542289, 0.0012751 , ..., 0.        , 1.        ,
        0.        ],
       [0.        , 0.38507463, 0.21586661, ..., 0.        , 0.        ,
        1.        ],
       [0.        , 0.35422886, 0.01031041, ..., 0.        , 0.        ,
        1.        ],
       ...,
       [0.        , 0.11293532, 0.03780868, ..., 0.        , 1.        ,
        0.        ],
       [1.        , 0.55870647, 0.03321025, ..., 0.        , 0.        ,
        1.        ],
       [0.        , 0.86965174, 0.78764136, ..., 0.        , 0.        ,
        0.        ]])

from sklearn.decomposition import PCA
model = PCA()

model.fit(x_train,y_train)

PCA()

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model.fit_transform(x)

array([[-2.25366835e+03, -1.54045960e+01,  2.41138473e+00, ...,
        -1.51660698e-13, -5.75917529e-14,  8.49111474e-14],
       [-3.93853721e+02, -4.44308307e+00,  1.11164960e+00, ...,
        -1.39011273e-13, -5.43819132e-13, -2.50122100e-13],
       [-2.17516400e+03,  7.87670140e+00,  1.15303139e+00, ...,
         4.61012805e-13, -3.30919668e-13, -3.11723294e-13],
       ...,
       [-1.93708238e+03, -1.84016387e+01,  2.46992124e+00, ...,
        -8.50874545e-14,  2.89204519e-14, -1.14256834e-14],
       [-1.97654365e+03,  2.67272984e+01,  2.46058887e-01, ...,
        -9.49510228e-14,  9.17079680e-14, -3.79921907e-14],
       [ 4.56138245e+03,  1.41903237e+00, -4.40312995e-01, ...,
        -1.24732051e-13,  9.92906304e-14, -1.56826950e-14]])

from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()
model=lr.fit(x_train,y_train)
pred=model.predict(x_test)
pred

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)

from sklearn.metrics import mean_squared_error,confusion_matrix,classification_report,accuracy_score
from sklearn.metrics import roc_curve,roc_auc_score,accuracy_score,mean_absolute_error
from sklearn.metrics import r2_score,mean_squared_error

print('classification_report')
print(classification_report(y_test,pred))

classification_report
              precision    recall  f1-score   support

           0       0.85      0.90      0.88      1046
           1       0.66      0.55      0.60       361

    accuracy                           0.81      1407
   macro avg       0.76      0.73      0.74      1407
weighted avg       0.81      0.81      0.81      1407

print('accuracy_score')
print(accuracy_score(y_test,pred))

accuracy_score
0.8130774697938877

print('confusion_matrix')
print(confusion_matrix(y_test,pred))

confusion_matrix
[[944 102]
 [161 200]]

print('mean_squared_error')
print(mean_squared_error(y_test,pred))

mean_squared_error
0.1869225302061123

print('roc_curve')
print(roc_curve(y_test,pred))

roc_curve
(array([0.        , 0.09751434, 1.        ]), array([0.        , 0.55401662, 1.        ]), array([2, 1, 0], dtype=int64))

print('roc_auc_score')
print(roc_auc_score(y_test,pred))

roc_auc_score
0.7282511400772234

from sklearn import metrics

XYZ = lr.predict_proba(x_test)[::,1]


fpr, tpr, _ = metrics.roc_curve(y_test,  XYZ)

#create ROC curve
plt.plot(fpr,tpr)
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

print('accuracy_score')
print(accuracy_score(y_test,pred))

accuracy_score
0.8130774697938877

print('mean_absolute_error')
print(mean_absolute_error(y_test,pred))

mean_absolute_error
0.1869225302061123

print('r2_score')
print(r2_score(y_test,pred))

r2_score
0.02003410962749541

print('mean_squared_error')
print(mean_squared_error(y_test,pred))

mean_squared_error
0.1869225302061123

from sklearn.ensemble import GradientBoostingClassifier
model = GradientBoostingClassifier()

model.fit(x_test,y_test)

GradientBoostingClassifier()

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model.score(x_test,y_test)

0.8869936034115139

y_predicted = model.predict(x_test)
from sklearn.metrics import confusion_matrix

CM = confusion_matrix(y_test,y_predicted)
CM

array([[1004,   42],
       [ 117,  244]], dtype=int64)

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.90      0.96      0.93      1046
           1       0.85      0.68      0.75       361

    accuracy                           0.89      1407
   macro avg       0.87      0.82      0.84      1407
weighted avg       0.88      0.89      0.88      1407

plt.figure(figsize = (5,3))

sns.heatmap(CM, annot = True)

plt.xlabel("Predicted")
plt.ylabel("Truth")

Text(33.22222222222222, 0.5, 'Truth')

import tensorflow
from tensorflow import keras
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(3,activation = 'sigmoid',input_dim =50))
model.add(Dense(1,activation = 'sigmoid'))

model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 3)                   │             153 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 1)                   │               4 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 157 (628.00 B)

 Trainable params: 157 (628.00 B)

 Non-trainable params: 0 (0.00 B)

(503)+3 = 153 __ Nural Network__ (31)+1 =4

model.compile(loss = 'binary_crossentropy',optimizer = 'Adam')

history = model.fit(x_train,y_train, epochs = 10,validation_split = 0.2)

Epoch 1/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 3s 5ms/step - loss: 0.5621 - val_loss: 0.5353
Epoch 2/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.5435 - val_loss: 0.5085
Epoch 3/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.5249 - val_loss: 0.4995
Epoch 4/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.5088 - val_loss: 0.4964
Epoch 5/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.5063 - val_loss: 0.4822
Epoch 6/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.4996 - val_loss: 0.4748
Epoch 7/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.4954 - val_loss: 0.4680
Epoch 8/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.4954 - val_loss: 0.4767
Epoch 9/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.4844 - val_loss: 0.4740
Epoch 10/10
141/141 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4943 - val_loss: 0.4747

history.history

{'loss': [0.557719886302948,
  0.5357632040977478,
  0.5170425772666931,
  0.5098909139633179,
  0.5036367177963257,
  0.4997670650482178,
  0.49613064527511597,
  0.49850934743881226,
  0.4926097095012665,
  0.49803227186203003],
 'val_loss': [0.5352861881256104,
  0.5085173845291138,
  0.4994664192199707,
  0.4964390993118286,
  0.48221153020858765,
  0.474754273891449,
  0.46796777844429016,
  0.47672298550605774,
  0.47400644421577454,
  0.4747053384780884]}

model.layers[0].get_weights()

[array([[-0.5463179 ,  0.05650653,  0.3650551 ],
        [ 0.03742829, -0.14135025,  0.13952711],
        [-0.06450991,  0.06037767, -0.00681495],
        [-0.17995971, -0.28102002, -0.1785467 ],
        [-0.00374536, -0.19732894, -0.4361244 ],
        [-0.04328292, -0.14842427,  0.11460909],
        [ 0.26719922,  0.03449143,  0.01766997],
        [ 0.3253568 ,  0.12997107,  0.17704494],
        [ 0.01427152, -0.04898315, -0.2847259 ],
        [ 0.10231312, -0.02309847, -0.4784282 ],
        [-0.22372206, -0.28719616, -0.2697603 ],
        [-0.09915258, -0.08608794,  0.08483568],
        [-0.23850799,  0.03314799, -0.17219527],
        [-0.04669595,  0.05355498, -0.07766202],
        [ 0.33629373, -0.02160749,  0.06255865],
        [ 0.26900995, -0.32996234, -0.09059858],
        [ 0.03602493, -0.20280282, -0.45306292],
        [-0.08447246,  0.21260592, -0.14637433],
        [ 0.1530685 ,  0.08943116, -0.3217084 ],
        [-0.02512531, -0.19206329,  0.04147688],
        [ 0.43579125, -0.17336755, -0.23017651],
        [-0.42269108, -0.07303046,  0.74278957],
        [-0.14707884, -0.04528806, -0.5965806 ],
        [-0.0638312 ,  0.18509586, -0.14188132],
        [ 0.0486447 , -0.06334215, -0.54371154],
        [ 0.14677532,  0.3282468 , -0.4293225 ],
        [-0.27745435, -0.3757026 ,  0.15246469],
        [-0.25772613,  0.32732505, -0.59927493],
        [ 0.1539736 ,  0.3636654 , -0.3323758 ],
        [-0.05427227,  0.19733638,  0.30169678],
        [ 0.02738924, -0.28589797, -0.20484433],
        [-0.48669294, -0.32840505, -0.05018048],
        [ 0.12742779,  0.1878976 ,  0.31367347],
        [ 0.34434706,  0.12863828, -0.1261132 ],
        [-0.09824098, -0.177224  , -0.629373  ],
        [ 0.1405677 , -0.24785283, -0.32302347],
        [ 0.31714106,  0.19785787, -0.32872766],
        [-0.40762943, -0.02202698, -0.01933847],
        [ 0.04765834,  0.15024097, -0.30880463],
        [-0.05138935,  0.02901016, -0.5496591 ],
        [ 0.02611189,  0.04118262,  0.0641803 ],
        [ 0.26618567,  0.13252485, -0.17799418],
        [ 0.43030825,  0.37173033, -0.8854663 ],
        [ 0.1212143 ,  0.14268833, -0.8258031 ],
        [ 0.25864804,  0.4541043 , -0.8002766 ],
        [-0.38748187, -0.08299001, -0.0490102 ],
        [ 0.02894197,  0.20624974, -0.4041168 ],
        [ 0.37240303,  0.1631575 , -0.11515518],
        [-0.48053962, -0.26066142,  0.41042686],
        [ 0.44895425,  0.01954965, -0.67026377]], dtype=float32),
 array([ 0.07838272, -0.00113499, -0.23922464], dtype=float32)]

model.layers[1].get_weights()

[array([[-0.35099477],
        [-1.2909899 ],
        [ 1.4429581 ]], dtype=float32),
 array([-0.40447542], dtype=float32)]

model.predict(x_test)

44/44 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step

array([[0.15514916],
       [0.15505856],
       [0.23293753],
       ...,
       [0.15505846],
       [0.15505846],
       [0.1550587 ]], dtype=float32)

To convert in to "0" , "1" we have to give a threshhold value

y_log = model.predict(x_test)

44/44 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step

np.where(y_log>0.5,1,0)

array([[0],
       [0],
       [0],
       ...,
       [0],
       [0],
       [0]])

from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_predicted)

0.8869936034115139

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])

[<matplotlib.lines.Line2D at 0x250edc37910>]

print(classification_report(y_test,y_predicted))

              precision    recall  f1-score   support

           0       0.90      0.96      0.93      1046
           1       0.85      0.68      0.75       361

    accuracy                           0.89      1407
   macro avg       0.87      0.82      0.84      1407
weighted avg       0.88      0.89      0.88      1407